SIAM Conference on Applied and Computational Discrete Algorithms (ACDA27)

About the Conference

This is the conference of the SIAM Activity Group on Applied & Computational Discrete Algorithms. ACDA27 brings together researchers who design and study combinatorial and graph algorithms motivated by applications. ACDA27 is organized by SIAM under the auspices of its Activity Group on Applied and Computational Discrete Algorithms. ACDA27 includes in its scope all topics where models and algorithms from discrete mathematics are applied to solve problems in computer science, scientific computing, data science, physical sciences, life sciences, engineering, social and information sciences, etc. ACDA27 invites papers on the formulation of combinatorial problems from applications; theoretical analyses; design of algorithms; computational evaluation of the algorithms; and deployment of the resulting software to enable applications.

The ACDA27 conference will include refereed proceedings as well as talks without proceedings papers (in the conventional SIAM style of conferences), posters, tutorials, invited talks, and industry sessions. Awards will be given for best paper, best poster, and best student presentation. Proceedings of the first three ACDA conferences organized in 2021, 2023, and 2025 are available here.  

The following conference will be held jointly:
SIAM Activity Group on Computational Science and Engineering (CSE27) 

The following workshop will be held jointly:
SIAM International Meshing Roundtable Workshop 2027 (IMR27)

Connect with other attendees on LinkedIn. 

Included Themes

Topics of interest include, but are not limited to, discrete or combinatorial problems and algorithms arising in:

• Algorithm engineering
• Algorithmic differentiation (AD)
• Combinatorial optimization and mathematical programming, including scheduling and resource allocation problems
• Combinatorial scientific computing (CSC), including models, algorithms, applications, numerical methods, and problems arising in data analysis
• Computational biology and bioinformatics
• Data management and data science
• Design and analysis of application-inspired exact, approximation, randomized, streaming, and learning-augmented algorithms
• Graph and hypergraph algorithms, including problems arising in network science and complex networks
• Interaction between algorithms and modern computing platforms, including challenges arising from memory hierarchies, accelerators, and novel memory technologies
• Machine learning and statistical methods for solving combinatorial problems
• Numerical linear algebra, including sparse matrix computations and randomized approaches
• Parallel and distributed computing, including algorithms, architectures, distributed systems, and all parallelism ranging from instruction-level and multi-core all the way to clouds and Exascale computing
• Other applications arising from security, computational finance, computational chemistry/physics, quantum computing, etc.